1. Field of the Invention
This invention relates to a method and an apparatus for processing the information, a program storage medium, and to a program. More particularly, it relates to a method and an apparatus for processing the information, a program storage medium, and to a program, in which the learning efficiency may be improved and the scale may be extended readily.
2. Description of Related Art
Up to now, a neural network has been studied as one model pertinent to the brain of the human being or an animal. In the neural network, a preset movement pattern may be learned at the outset to check whether or not input data corresponds to the learned movement model.
As the models for recognizing the movement pattern, a movement pattern learning model by a local expression scheme and a movement pattern learning model by a distributed expression scheme are known.
With the movement pattern learning model by the local expression scheme, independent local modules 1-1 to 1-3 are interconnected via associated gates 2-1 to 2-3, as shown in FIG. 1. The local modules 1-1 to 1-3 learn independent movement patterns.
In the movement pattern learning model by the local expression scheme, the totality of the outputs is determined by controlling the gates 2-1 to 2-3.
This movement pattern learning model by the local expression scheme is disclosed in the Patent Publication 1.
On the other hand, in the movement pattern learning model by the distributed expression scheme, a module 21 learns plural movement patterns, as shown in FIG. 2.
[Patent Publication 1] Japanese Laid-Open Patent Publication H-11-126198
However, in the movement pattern learning model by the local expression scheme, the relationship among different patterns is not taken into account, and hence it is difficult to generalize the plural patterns for learning.
In the movement pattern learning model by the distributed expression scheme, plural movement patterns are learned by a sole module having only a small number of parameters, and hence the learning efficiency is low, whilst difficulties are met in connection with scale extensibility.